LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain

LIO_SAM for 6-axis IMU and GNSS.

Robust LiDAR SLAM with a versatile plug-and-play loop closing and pose-graph optimization.

The tight integration of FAST-LIO with Radius-Search-based loop closure module.

ROS 2 package of tightly-coupled lidar inertial ndt/gicp slam

For an education purpose, from-scratch, single-file, python-only pose-graph optimization implementation

A collection of GTSAM factors and optimizers for point cloud SLAM

[TMECH'2024] Official codes of the paper. PALoc: Advancing SLAM Benchmarking with Prior-Assisted 6-DoF Trajectory Generation and Uncertainty Estimation

[ICRA@40] MS-Mapping: An Uncertainty-Aware Large-Scale Multi-Session LiDAR Mapping System

Visual Inertial Odometry (VIO) / Simultaneous Localization & Mapping (SLAM) using iSAM2 framework from the GTSAM library.

Factored inference for discrete-continuous smoothing and mapping.

Robust GNSS Processing With Factor Graphs

Lightweighted graph optimization (Factor graph) library.

learning and feeling SLAM together with hands-on-experiments

LIO-SAM-6AXIS with intensity image loop optimization

Software Release for "Incremental Covariance Estimation for Robust Localization"

The full_linear_wheel_odometry_factor provides motion constraints and online calibration for skid-steering robots. This constraint can be incorporated into your SLAM framework. Here is an example video using this factor. https://youtu.be/Vss86xUhU80

Code release for "Evaluation of Precise Point Positioning Convergence with an Incremental Graph Optimizer".

Lidar localization system with prior map constraint and lio constraint based on GTSAM

IMU-based human skeletal pose estimation in C++11